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In this study, the XGBoost algorithm successfully diagnosed polycystic ovary syndrome with an accuracy of 0.87 using a dataset from Kerala, suggesting its usefulness for classification problems in healthcare.

This study found that the DNN-DTIs prediction model achieved high accuracy in predicting drug-target interactions, suggesting its potential application in drug repositioning and the discovery of new uses for existing drugs.

In this study, researchers developed a predictive model for the onset of alopecia areata by analyzing six datasets to identify key feature genes and employing various machine learning algorithms, ultimately finding the XGBoost model most effective for clinical application.

This study found that a data-driven model using XGBoost effectively predicts individualized responses to minoxidil for androgenetic alopecia, outperforming traditional methods in accuracy and reliability.

In this study, machine learning models showed high accuracy in predicting therapeutic outcomes for female pattern hair loss, highlighting the significant impact of age of onset and condition duration on treatment response.